J Cheminform. 2025 Feb 4;17(1):16. doi: 10.1186/s13321-025-00962-0.

ABSTRACT

INTRODUCTION: Drug repositioning offers numerous advantages, such as faster development timelines, reduced costs, and lower failure rates in drug development. Supervised machine learning is commonly used to score drug candidates but is hindered by the lack of reliable negative data-drugs that fail due to inefficacy or toxicity- which is difficult to access, lowering their prediction accuracy and generalization. Positive-Unlabeled (PU) learning has been used to overcome this issue by either randomly sampling unlabeled drugs or identifying probable negatives but still suffers from misclassification or oversimplified decision boundaries.

RESULTS: We proposed a novel strategy using Large Language Models (GPT-4) to analyze all clinical trials on prostate cancer and systematically identify true negatives. This approach showed remarkable improvement in predictive accuracy on independent test sets with a Matthews Correlation Coefficient of 0.76 (± 0.33) compared to 0.55 (± 0.15) and 0.48 (± 0.18) for two commonly used PU learning approaches. Using our labeling strategy, we created a training set of 26 positive and 54 experimentally validated negative drugs. We then applied a machine learning ensemble to this new dataset to assess the repurposing potential of the remaining 11,043 drugs in the DrugBank database. This analysis identified 980 potential candidates for prostate cancer. A detailed review of the top 30 revealed 9 promising drugs targeting various mechanisms such as genomic instability, p53 regulation, or TMPRSS2-ERG fusion.

CONCLUSION: By expanding our negative data labeling approach to all diseases within the ClinicalTrials.gov database, our method could greatly advance supervised drug repositioning, offering a more accurate and data-driven path for discovering new treatments.

PMID:39905466 | DOI:10.1186/s13321-025-00962-0

Paediatr Anaesth. 2025 Feb 4. doi: 10.1111/pan.15071. Online ahead of print.

NO ABSTRACT

PMID:39905653 | DOI:10.1111/pan.15071

Anal Chem. 2025 Feb 5. doi: 10.1021/acs.analchem.4c05046. Online ahead of print.

ABSTRACT

Artificial intelligence (AI), especially through the development of deep learning techniques like convolutional neural networks (CNNs), has revolutionized numerous fields. CNNs, introduced by Yann LeCun in the 1990s (Hubbard, W.; Jackel, L. D. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1989, 1 (4), 541- 551. https://doi.org/10.1162/neco.1989.1.4.541), have found applications in healthcare for medical diagnostics, autonomous vehicles in transportation, stock market prediction in finance, and image recognition in computer vision to name just a few. Similarly, in analytical chemistry, deep learning has enhanced data analysis from techniques like MS spectrometry, NMR, fluorescence spectroscopy, and chromatography. Another AI branch, Natural Language Processing (NLP), has surged recently with the advent of Large Language Models (LLMs), such as OpenAI's ChatGPT. This paper demonstrates the application of an LLM via a smartphone to conduct multivariate data analyses, in an interactive conversational manner, of a hyper-spectral imaging data set from laser-induced breakdown spectroscopy (LIBS). We demonstrate the potential of LLMs to process and analyze data sets, which automatically generate and execute code in response to user queries, and anticipate their growing role in the future of analytical chemistry.

PMID:39907023 | DOI:10.1021/acs.analchem.4c05046

Health Data Sci. 2025 Feb 4;5:0221. doi: 10.34133/hds.0221. eCollection 2025.

ABSTRACT

Background: The electrocardiogram (ECG) is a valuable, noninvasive tool for monitoring heart-related conditions, providing critical insights. However, the interpretation of ECG data alongside patient information demands substantial medical expertise and resources. While deep learning methods help streamline this process, they often fall short in integrating patient data with ECG readings and do not provide the nuanced clinical suggestions and insights necessary for accurate diagnosis. Methods: Although recent advancements in multi-modal large language modeling have propelled their application scope beyond the natural language processing domain, their applicability to ECG processing remains largely unexplored, partly due to the lack of text-ECG data. To this end, we develop ECG-Language Model (ECG-LM), the first multi-modal large language model able to process natural language and understand ECG signals. The model employs a specialized ECG encoder that transforms raw ECG signals into a high-dimensional feature space, which is then aligned with the textual feature space derived from the large language model. To address the scarcity of text-ECG data, we generated text-ECG pairs by leveraging detailed ECG pattern descriptions from medical guidelines, creating a robust dataset for pre-training ECG-LM. Additionally, we fine-tune ECG-LM with public clinical conversation datasets and build an additional supervised fine-tuning dataset based on real clinical data from the hospital, aiming to provide a more comprehensive and customized user experience. Results: ECG-LM outperforms existing few-shot and zero-shot solutions in cardiovascular disease detection across all 3 tasks (diagnostic, rhythm, and form) while also demonstrating strong potential in ECG-related question answering. Conclusions: The results across various tasks demonstrate that ECG-LM effectively captures the intricate features of ECGs, showcasing its versatility in applications such as disease prediction and advanced question answering.

PMID:39906894 | PMC:PMC11791404 | DOI:10.34133/hds.0221

Heliyon. 2025 Jan 15;11(2):e41974. doi: 10.1016/j.heliyon.2025.e41974. eCollection 2025 Jan 30.

ABSTRACT

This systematic review examines the application of artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for climate change adaptation and mitigation in Iran, Pakistan, and Turkey. These three nations-key Economic Cooperation Organization (ECO) members and a nexus between Europe and South Asia-are experiencing diverse environmental challenges due to varying climatic conditions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search in the Scopus database, ultimately identifying 76 relevant articles out of an initial 492. Although some articles utilized multiple techniques, classical ML methods appeared in approximately 37.3 % of the studies, neural network paradigms in about 57.5 %, and optimization or meta-heuristic algorithms in around 5.0 %. Regarding thematic focus, about 33.3 % of the articles addressed water resource management, 22.2 % focused on climate prediction, 11.1 % on land and agriculture, 9 % on ecosystem modeling, and 24.2 % on natural disaster preparedness and response. The analysis reveals a growing but uneven body of research utilizing AI across the ECO countries. By highlighting successful applications, identifying key gaps-such as limited cross-border collaboration and inconsistent data availability-and proposing a framework for more integrated research, this review aims to guide future initiatives that leverage AI's potential to improve climate resilience and sustainability in the region.

PMID:39906868 | PMC:PMC11791260 | DOI:10.1016/j.heliyon.2025.e41974

Heliyon. 2025 Jan 16;11(2):e41973. doi: 10.1016/j.heliyon.2025.e41973. eCollection 2025 Jan 30.

ABSTRACT

Coronary artery disease (CAD) is a leading cause of global mortality, often involving the development of atherosclerotic plaques in coronary arteries, particularly at bifurcation sites. Percutaneous coronary intervention (PCI) of bifurcation lesions presents challenges, necessitating accurate assessment of hemodynamic parameters such as wall shear stress (WSS) and oscillatory shear index (OSI) to predict acute coronary syndrome (ACS) risk. Computational fluid dynamics (CFD) provides valuable insights but is computationally intensive, prompting exploration of machine learning (ML) models for efficient hemodynamics prediction. This study aims to bridge the gap in understanding the influence of stenosis severity and location on hemodynamics in the left coronary artery (LCA) bifurcation by integrating ML algorithms with comprehensive CFD simulations, thereby enhancing non-invasive prediction of complex hemodynamics. An extensive dataset of 6858 synthetic LCA geometries with varying plaque severities and locations was generated for analysis. Hemodynamic parameters (TAWSS and OSI) were computed using CFD simulations and utilized for ML model training. Fourteen ML algorithms were employed for regression analysis, and their performance was evaluated using multiple metrics. The Decision Tree Regressor and K Nearest Neighbors models demonstrated the most effective prediction of TAWSS and OSI parameters, aligning well with CFD simulation results. The Decision Tree Regressor showed minimal prediction discrepancies (TAWSS: R2 = 0.998952, MAE = 0.000587, RMSE = 0.001626; OSI: R2 = 0.961977, MAE = 0.022264, RMSE = 0.041411) offering rapid and reliable assessments of hemodynamic conditions in the LCA bifurcation. Integration of ML algorithms with comprehensive CFD simulations provides a promising approach to enhance the non-invasive prediction of complex hemodynamics in the LCA bifurcation. The ability to efficiently predict hemodynamic parameters could significantly aid medical practitioners in time-sensitive clinical settings, offering valuable insights for coronary artery disease management. Further research is warranted to evaluate the effectiveness of deep learning models and address challenges in patient-specific applications.

PMID:39906857 | PMC:PMC11791239 | DOI:10.1016/j.heliyon.2025.e41973

Heliyon. 2025 Jan 13;11(2):e41835. doi: 10.1016/j.heliyon.2025.e41835. eCollection 2025 Jan 30.

ABSTRACT

BACKGROUND: Machine learning has tremendous potential in acute medical care, particularly in the field of precise medical diagnosis, prediction, and classification of brain tumors. Malignant gliomas, due to their aggressive growth and dismal prognosis, stand out among various brain tumor types. Recent advancements in understanding the genetic abnormalities that underlie these tumors have shed light on their histo-pathological and biological characteristics, which support in better classification and prognosis.

OBJECTIVES: This review aims to predict gene alterations and establish structured correlations among various tumor types, extending the prediction of genetic mutations and structures using the latest machine learning techniques. Specifically, it focuses on multi-modalities of Magnetic Resonance Imaging (MRI) and histopathology, utilizing Convolutional Neural Networks (CNN) for image processing and analysis.

METHODS: The review encompasses the most recent developments in MRI, and histology image processing methods across multiple tumor classes, including Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma. It identifies challenges in tumor classification, segmentation, datasets, and modalities, employing various neural network architectures. A competitive analysis assesses the performance of CNN. Furthermore it also implies K-MEANS clustering to predict Genetic structure, Genes Clusters prediction and Molecular Alteration of various types and grades of tumors e.g. Glioma, Meningioma, Pituitary, Oligodendroglioma, and Astrocytoma.

RESULTS: CNN and KNN structures, with their ability to extract highlights in image-based information, prove effective in tumor classification and segmentation, surmounting challenges in image analysis. Competitive analysis reveals that CNN and outperform others algorithms on publicly available datasets, suggesting their potential for precise tumor diagnosis and treatment planning.

CONCLUSION: Machine learning, especially through CNN and SVM algorithms, demonstrates significant potential in the accurate diagnosis and classification of brain tumors based on imaging and histo-pathological data. Further advancements in this area hold promise for improving the accuracy and efficiency of intra-operative tumor diagnosis and treatment.

PMID:39906822 | PMC:PMC11791217 | DOI:10.1016/j.heliyon.2025.e41835

Heliyon. 2025 Jan 17;11(2):e41779. doi: 10.1016/j.heliyon.2025.e41779. eCollection 2025 Jan 30.

ABSTRACT

Falling from height (FFH) is a major cause of injuries and fatalities on construction sites. Research has emphasized the role of technological advances in managing FFH safety risks. In this investigation, the objective is to forecast if a laborer is operating at an elevated position by utilizing an accelerometer, gyroscope, and pressure information through the application of deep-learning techniques. The study involved analyzing worker data to quickly implement safety measures for working at heights. A total of 45 analyses were conducted using DNN, CNN, and LSTM deep-learning models, with 5 different window sizes and 3 different overlap rates. The analysis revealed that the DNN model, utilizing a 1-s window size and a 75 % overlap rate, attained an accuracy of 94.6 % with a loss of 0.1445. Conversely, the CNN model, employing a 5-s window size and a 75 % overlap rate, demonstrated an accuracy of 94.9 % with a loss of 0.1696. The results of this study address information gaps by efficiently predicting workers' working conditions at heights without the need for complex calculations. By implementing this method at construction sites, it is expected to reduce the risk of FFH and align occupational health and safety practices with technological advancements.

PMID:39906815 | PMC:PMC11791131 | DOI:10.1016/j.heliyon.2025.e41779

Heliyon. 2025 Jan 20;11(2):e42119. doi: 10.1016/j.heliyon.2025.e42119. eCollection 2025 Jan 30.

ABSTRACT

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy muscles from abnormal ones. Existing analysis applications, designed for other purposes, often lack essential software engineering features such as a user-friendly interface, infrastructure independence, usability and learning ability, cloud computing capabilities, and AI-based assistance. This research proposes a computer-based methodology to analyze human motion and differentiate between healthy and unhealthy muscles. First, an IoT-based approach is proposed to digitize human motion using smartphones instead of hardly accessible wearable sensors and markers. The motion data is then simulated to analyze the neuromusculoskeletal system. An agent-driven modeling method ensures the naturalness, accuracy, and interpretability of the simulation, incorporating neuromuscular details such as Henneman's size principle, action potentials, motor units, and biomechanical principles. The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. A user-friendly graphical interface enhances the application's usability. Being fully cloud-based, the application is infrastructure-independent and can be accessed on smartphones, PCs, and other devices without installation. This strategy not only addresses the current challenges in treating motion disorders but also paves the way for other clinical simulations by considering both scientific and computational requirements.

PMID:39906796 | PMC:PMC11791118 | DOI:10.1016/j.heliyon.2025.e42119

Cureus. 2025 Feb 2;17(2):e78388. doi: 10.7759/cureus.78388. eCollection 2025 Feb.

ABSTRACT

This study evaluates the efficiency and dosimetric performance of RatoGuide, an artificial intelligence (AI)-assisted radiotherapy planning tool, by comparing AI-generated and manually created treatment plans for a 50-year-old male with right-sided oropharyngeal cancer (cT2N2bM0, cStage IVA) who underwent concurrent chemoradiotherapy. Treatment plans were created using volumetric-modulated arc therapy (VMAT) following the approach used by the Japanese Clinical Oncology Group (JCOG) protocol. RatoGuide generated two plans: one prioritizing the planning target volume (PTV) and the other focusing on organs at risk (OAR), while an experienced radiation oncologist manually developed a plan using a treatment planning system (TPS). Dosimetric comparisons focused on target coverage, OAR sparing, and dose homogeneity. Results showed that both AI-generated and TPS plans achieved comparable PTV coverage, with nearly identical values for Dmin, Dmean, and Dmax. The TPS plan exhibited slightly better dose homogeneity, whereas the AI-generated plan provided superior OAR sparing, particularly for the spinal cord and parotid glands, reducing the spinal cord's intermediate-dose volume (V30) by approximately 40%. However, the AI plan yielded slightly higher mean doses to both submandibular glands, though still within clinically acceptable thresholds. Additionally, the AI planning workflow was completed in just 30 minutes, significantly reducing the time required for manual planning. RatoGuide demonstrated efficiency in generating high-quality treatment plans, achieving comparable PTV coverage, and improving OAR sparing in certain areas. However, minor refinements are needed to optimize dose homogeneity and further minimize submandibular gland exposure. These findings suggest that AI-assisted planning has the potential to enhance radiotherapy efficiency and consistency.

PMID:39906643 | PMC:PMC11793990 | DOI:10.7759/cureus.78388

J Med Imaging (Bellingham). 2025 Jan;12(1):014003. doi: 10.1117/1.JMI.12.1.014003. Epub 2025 Feb 3.

ABSTRACT

PURPOSE: The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities such as computed tomography. Recently, deep learning approaches have demonstrated the potential to enhance image quality beyond classic limits; however, most deep learning models attempt a blind restoration problem and base their restoration on image inputs alone without direct knowledge of the image noise and blur properties. We present a method that leverages both degraded image inputs and a characterization of the system's blur and noise to combine modeling and deep learning approaches.

APPROACH: Different methods to integrate these auxiliary inputs are presented, namely, an input-variant and a weight-variant approach wherein the auxiliary inputs are incorporated as a parameter vector before and after the convolutional block, respectively, allowing easy integration into any convolutional neural network architecture.

RESULTS: The proposed model shows superior performance compared with baseline models lacking auxiliary inputs. Evaluations are based on the average peak signal-to-noise ratio and structural similarity index measure, selected examples of top and bottom 10% performance for varying approaches, and an input space analysis to assess the effect of different noise and blur on performance. In addition, the proposed model exhibits a degree of robustness when the blur and noise parameters deviate from their true values.

CONCLUSION: Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.

PMID:39906485 | PMC:PMC11788843 | DOI:10.1117/1.JMI.12.1.014003

Front Bioeng Biotechnol. 2025 Jan 21;13:1506508. doi: 10.3389/fbioe.2025.1506508. eCollection 2025.

ABSTRACT

Protein function prediction is crucial in several key areas such as bioinformatics and drug design. With the rapid progress of deep learning technology, applying protein language models has become a research focus. These models utilize the increasing amount of large-scale protein sequence data to deeply mine its intrinsic semantic information, which can effectively improve the accuracy of protein function prediction. This review comprehensively combines the current status of applying the latest protein language models in protein function prediction. It provides an exhaustive performance comparison with traditional prediction methods. Through the in-depth analysis of experimental results, the significant advantages of protein language models in enhancing the accuracy and depth of protein function prediction tasks are fully demonstrated.

PMID:39906415 | PMC:PMC11790633 | DOI:10.3389/fbioe.2025.1506508

Heliyon. 2024 Dec 30;11(2):e41311. doi: 10.1016/j.heliyon.2024.e41311. eCollection 2025 Jan 30.

ABSTRACT

Cumulative evidence suggests a link between specific autoimmune diseases (AD), including idiopathic inflammatory myopathies (IIM), and SARS-CoV-2 infection or COVID-19 vaccination. Anti-synthetase syndrome (ASS), a subset of IIM, is defined by the presence of autoantibodies against aminoacyl-tRNA synthetase (anti-ARS) and is strongly associated with interstitial lung disease (ILD), a major contributor to severe complications and reduced survival. We present four clinical cases of patients who developed autoantibodies against threonyl (PL-7) and alanyl (PL-12) synthetases associated with ASS-ILD shortly after SARS-CoV-2 infection or COVID-19 vaccination. Anti-ARS autoantibodies were identified using three complementary methods: immunoblotting, western blotting (WB) and the method considered the gold standard, immunoprecipitation (IP), which ensures accurate interpretation of results. The study highlights the clinical and pathogenic overlap between ASS-ILD and SARS-CoV-2-related lung involvement.Both conditions share similar high-resolution computed tomography (HRCT) patterns, including inflammation and pulmonary fibrosis (PF), driven by IFN-γ signaling, which complicates accurate diagnosis. Our results provide novel insights into the temporal association of SARS-CoV-2 and vaccine exposure with ASS-ILD, focusing on possible molecular mimicry between viral proteins and ARS molecules as a potential mechanism. Understanding the involvement of specific anti-ARS autoantibodies (PL-7 and PL-12) and the identification of genetic predispositions (HLA-B∗08:01 and HLA-DRB1∗03:01) in these patients may be key to underpinning these autoimmune manifestations. The study underscores the importance of a multidisciplinary approach and vigilant follow-up to optimize diagnosis and management. Further research is essential to elucidate the causal relationships and molecular mechanisms behind these observations.

PMID:39906838 | PMC:PMC11791273 | DOI:10.1016/j.heliyon.2024.e41311

Respirology. 2025 Feb 4. doi: 10.1111/resp.14892. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Fibrotic interstitial lung disease (ILD) is associated with high morbidity and mortality. Patients often exhibit impaired nutritional status and alterations in body composition, such as dynapenia and sarcopenia, which correlate with poor pulmonary function, reduced exercise tolerance and diminished quality of life. However, the impact of dynapenia and sarcopenia on prognosis has not been examined extensively in ILD patients. We assessed the impact of dynapenia and sarcopenia as risk factors for mortality and their prevalence in ILD.

METHODS: Prospective cohort study. ILD was classified into idiopathic pulmonary fibrosis (IPF), connective tissue disease-related ILD (CTD-ILD) and chronic hypersensitivity pneumonitis (CHP). Patients over 18 years old with a confirmed diagnosis of ILD were included, while those with diagnoses of cancer, human immunodeficiency virus and neurological disease were excluded. Dynapenia and sarcopenia were determined according to EWGSOP2 criteria.

RESULTS: Ninety-eight ILD patients were included; 33.66% had IPF, 47.96% had CTD-ILD, and 18.37% had CHP. The mean age was 63.89 ± 12.02 years; 37.76% were male. The risk factors associated with mortality included dynapenia (HR: 2.04, 95% CI: 1.10-3.77, p = 0.022), sarcopenia (HR: 1.88, 95% CI; 1.00-3.33, p = 0.049) and exercise tolerance (HR: 0.99, 95% CI; 0.99-0.99, p = 0.023), adjusted for confounding variables. The prevalence of dynapenia was 45% in ILD; 51% in IPF, 35% in CTD-ILD and 61% in CHP. The prevalence of sarcopenia was 29%; both IPF (39%) and CHP (50%) had a higher prevalence of sarcopenia than CTD-ILD (14%).

CONCLUSION: Sarcopenia and dynapenia are independent risk factors for mortality in ILD.

PMID:39905591 | DOI:10.1111/resp.14892

BMC Pulm Med. 2025 Feb 4;25(1):61. doi: 10.1186/s12890-025-03521-3.

ABSTRACT

BACKGROUND: Early identification of functional decline in fibrotic interstitial lung disease (F-ILD) is crucial for timely treatment and improved survival. While the 6-minute walk test (6MWT) is the standard for functional evaluation, it has practical limitations. The 1-minute sit-to-stand test (1MSTS) offers a simpler alternative; however, its correlation with the 6MWT in F-ILD patients remains unclear. This study aims to establish reference values for the 1MSTS in assessing functional capacity, evaluate its correlation with the 6MWT, and explore its utility in predicting 18-month mortality in F-ILD patients.

METHODS: This prospective study enrolled participants diagnosed with F-ILD based on multidisciplinary team discussions. Assessments included the 1MSTS, 6MWT, pulmonary function test (PFT), GAP score, mMRC scale, and Charlson Comorbidity Index (CCI). The association between 1MSTS repetitions and other variables was calculated using Spearman's rho. Bland-Altman plots assessed the agreement between 1MSTS repetitions and the 6MWT. Predictors of 18-month mortality were evaluated using ROC curve and Kaplan-Meier curve.

RESULTS: Of the 150 F-ILD patients, 37 (24.6%) had idiopathic pulmonary fibrosis (IPF), and 113 (75.4%) had connective tissue disease-related ILD (CTD-ILD). Using ≤ 23 repetitions as the cutoff for functional impairment in 1MSTS, 74 (47.3%) patients were classified as impaired. The 1MSTS significantly predicted 18-month mortality and demonstrated moderate correlations with GAP score (rs = -0.49), mMRC scale (rs = -0.47), and 6MWT distance (rs = 0.65). Bland-Altman analysis indicated agreement between 1MSTS repetitions and 6MWT distance. Using ≤ 23 repetitions as the cutoff value for the 1MSTS to predict 18-month mortality, the mortality rate was 76.4%, with an AUC of 0.81.

CONCLUSIONS: The findings suggest that ≤ 23 repetitions in the 1MSTS can serve as an indicator of functional impairment, demonstrate a good correlation with 6MWT distance, and effectively predict 18-month mortality in patients with F-ILD.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:39905346 | DOI:10.1186/s12890-025-03521-3

iScience. 2025 Jan 4;28(2):111758. doi: 10.1016/j.isci.2025.111758. eCollection 2025 Feb 21.

ABSTRACT

Bladder tumors with aggressive characteristics often present microenvironmental niches marked by low oxygen levels (hypoxia) and limited glucose supply due to inadequate vascularization. The molecular mechanisms facilitating cellular adaptation to these stimuli remain largely elusive. Employing a multi-omics approach, we discovered that hypoxic and glucose-deprived cancer cells enter a quiescent state supported by mitophagy, fatty acid β-oxidation, and amino acid catabolism, concurrently enhancing their invasive capabilities. Reoxygenation and glucose restoration efficiently reversed cell quiescence without affecting cellular viability, highlighting significant molecular plasticity in adapting to microenvironmental challenges. Furthermore, cancer cells exhibited substantial perturbation of protein O-glycosylation, leading to simplified glycophenotypes with shorter glycosidic chains. Exploiting glycoengineered cell models, we established that immature glycosylation contributes to reduced cell proliferation and increased invasion. Our findings collectively indicate that hypoxia and glucose deprivation trigger cancer aggressiveness, reflecting an adaptive escape mechanism underpinned by altered metabolism and protein glycosylation, providing grounds for clinical intervention.

PMID:39906564 | PMC:PMC11791300 | DOI:10.1016/j.isci.2025.111758

Front Plant Sci. 2025 Jan 20;16:1541642. doi: 10.3389/fpls.2025.1541642. eCollection 2025.

NO ABSTRACT

PMID:39906500 | PMC:PMC11788399 | DOI:10.3389/fpls.2025.1541642

JBMR Plus. 2025 Jan 30;9(3):ziae173. doi: 10.1093/jbmrpl/ziae173. eCollection 2025 Mar.

ABSTRACT

Activating internal tandem duplications (ITD) in the juxtamembrane domain of receptor tyrosine kinase FLT3 occur frequently in patients with acute myeloid leukemia (AML). Constitutive active FLT3-ITD mutations induce aberrant signaling and promote leukemic cell transformation. Inactivation of the attenuating receptor protein tyrosine phosphatase CD45 (PTPRC) in FLT3-ITD mice resulted in the development of a severe hematopoietic phenotype with characteristics of AML. In addition, abnormal bone structures and ectopic bone formation were observed in these mice, suggesting a previously unknown role of FLT3 to control bone development and remodeling. While Ptprc knockout and Flt3-ITD mutant mice showed a largely normal bone microarchitecture, micro-CT analysis of femurs from Flt3-ITD Ptprc knockout mice revealed trabecularization of the cortical bone. This resulted in increased trabecular bone volume at the metaphysis, while the cortical bone at the diaphysis was thinner and less dense. In the metaphysis, severely reduced osteoclast and osteoblast numbers were observed. Reduced capacity of ex vivo differentiation of CD11b-positive bone marrow stem cells to mature osteoclast was accompanied by their abnormal morphology and reduced size. Transcriptome analysis revealed reduced expression of osteoclastogenic genes. Unexpectedly, cumulative resorption activity of osteoclasts was increased. Size and structure of resorption pits of differentiated osteoclasts remained similar to those observed in osteoclast cultures derived from control animals. Enhanced proliferation of cells in osteoclast cultures derived from FLT3-ITD-expressing mice was mediated by increased expression of STAT5 target genes. Transcriptome analysis of differentiated osteoclasts showed dysregulated signaling pathways influencing their differentiation as well as the coupling of bone resorption and formation. Taken together, inactivation of attenuating CD45 in mice expressing oncogenic FLT3-ITD resulted in marked abnormalities of the osteo-hematopoietic niche, which can be explained by aberrant STAT5 activation.

PMID:39906260 | PMC:PMC11788565 | DOI:10.1093/jbmrpl/ziae173

Hortic Res. 2024 Oct 23;12(2):uhae291. doi: 10.1093/hr/uhae291. eCollection 2025 Jan.

ABSTRACT

The large diversity of grapevine cultivars includes genotypes more tolerant to water deficit than others. Widely distributed cultivars, like Merlot, are more sensitive to water deprivation than local cultivars like Callet, which are more adapted to water deficit due to their Mediterranean origin. Despite their tolerance, adaptation to water deficit influenced by grafting in rootstocks like 110 Richter is key to facing drought in vineyards, defining the scion-rootstock relationship. To understand these differences, we explored transcriptomic, metabolic, hormonal and physiological responses under three levels of water deficit (mild, high, and extreme), using 110 Richter as the rootstock in both cultivars. Results revealed that sensitivity to abscisic acid (ABA) is essential for water deficit tolerance in the aerial part, guiding root responses. Callet/110 Richter activates more gene expression patterns in response to ABA, reducing water loss compared to Merlot/110 Richter in both aerial and root parts. This modulation in Callet/110 Richter involves regulating metabolic pathways to increase cell turgor, reducing photosynthesis, and producing molecules like polyphenols or flavonoids to respond to oxidative stress. In contrast, Merlot/110 Richter shows a lack of specific response, especially in the roots, indicating less resilience to water stress. Therefore, selecting genotypes more sensitive to ABA and their interaction with rootstocks is key for managing vineyards in future climate change scenarios.

PMID:39906169 | PMC:PMC11789524 | DOI:10.1093/hr/uhae291

Environ Microbiome. 2025 Feb 4;20(1):17. doi: 10.1186/s40793-025-00677-7.

ABSTRACT

Urban green areas provide multiple ecosystem services in cities, mitigating environmental risks and providing a healthier environment for humans. Even if urban ecology has become popular in the last decade, the soil environment with its microbiota, which sustains many other biodiversity layers, remains overlooked. Here, a comprehensive database of scientific papers published in the last 30 years investigating different aspects of soil microbial diversity was built and systematically reviewed. The aim was to identify the taxa, experimental methods and geographical areas that have been investigated, and to highlight gaps in knowledge and research prospects. Our results show that current knowledge on urban soil microbiota remains incomplete, mainly due to the lack of publications on functional aspects, and is biased, in terms of investigated taxa, with most studies focused on Prokaryotes, and geographic representativeness, with the interest focused on a few large cities in the Northern hemisphere. By coupling bibliometrics with statistical modelling we found that soil microbial traits such as biomass and respiration and omics techniques attract the interest of the scientific community while multi-taxa and time-course studies appeal more to the general public.

PMID:39905522 | DOI:10.1186/s40793-025-00677-7